Identification of food groups for use in a self-administered, computer-assisted diet history interview for use in Australia

Burden, Sandy; Probst, Yasmine; Steel, David G; Tapsell, Linda C.

doi:10.1016/j.jfca.2008.10.004

Cited by 9 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the website used grouped food data (19) and to ensure comparability of the food data, interviewer administered assessment data from FoodWorks was transformed into grouped food data by assigning each food item into a corresponding food group from the website database using look-up tables in Microsoft Excel allowed the database code of each food item to be linked automatically to the database code of the correct food group. This then allowed comparisons of the food data to be made as both databases were based on grouped information originating from the AUSNUT database (18). Data from the 3-day food records were similarly analyzed in FoodWorks by the dietitian and transformed into grouped food data using the look-up table method.…”

Section: Statistical Analysesmentioning

confidence: 99%

Computerized dietary assessments compare well with interviewer administered diet histories for patients with type 2 diabetes mellitus in the primary healthcare setting

Probst

Faraji

Batterham

et al. 2008

Patient Education and Counseling

Self Cite

View full text Add to dashboard Cite

Computerized dietary assessments compare well with interviewer administered diet histories for patients with type 2 diabetes mellitus in the primary healthcare setting, Patient Education and Counseling, 72(1), 2008, 49-55. Copyright Elsevier 2008. Computerized dietary assessments compare well with interviewer administered diet histories for patients with type 2 diabetes mellitus in the primary healthcare setting AbstractUsing a context-based case-control trial, 41 adults with type 2 diabetes mellitus were randomized into four groups to complete dietary assessments (computerized or interviewer administered) at 0, 2 and 8 weeks and food records at 0 and 2 weeks. Repeatability of reported energy, total fat, saturated, polyunsaturated and monounsaturated fatty acids between the computerized and interviewer administered methods were assessed using repeated measures ANOVA. Paired t-tests and Pearson's correlations determined relative validity of the assessments. Methods: Using a context based case-control trial, forty-one adults with type 2 diabetes mellitus were randomized into four groups to complete dietary assessments (computerized or interviewer administered) at zero, two and eight weeks and food records at zero and two weeks. Repeatability of reported energy, total fat, saturated, polyunsaturated and monounsaturated fatty acids between the computerized and interviewer administered methods were assessed using repeated measures ANOVA. Paired t-tests and Pearson's correlations determined relative validity of the assessments.Results: Thirty-one patients completed all visits. Statistically significant differences were found between computerized and interviewer administered data for total fat (p=0.048) and saturated fatty acids (p=0.019) between zero and two weeks. Computerized assessments correlated better with food records (r=0.16-0.52) compared with interviewer administered assessments (r=-0.02-0.51). Conclusion:Computerized assessments saw a learning effect with repeated use indicating that users were becoming more familiar with the website with repeated use. Relative validity suggests that the website may capture more foods though this requires further investigation.Practice Implications: By allowing patients to self-report their intakes on a computer, dietitians will have the ability to spend increased time with their patients counseling them toward change.

show abstract

Section: Statistical Analysesmentioning

confidence: 99%

Computerized dietary assessments compare well with interviewer administered diet histories for patients with type 2 diabetes mellitus in the primary healthcare setting

Probst

Faraji

Batterham

et al. 2008

Patient Education and Counseling

Self Cite

View full text Add to dashboard Cite

show abstract

“…Variations in food choices could create inaccessible numbers of food choice combinations, but shortcuts are not the answer. Previous studies suggest that a food combination requires disaggregation into single food groups to provide a more precise intake distribution (18)(19)(20)(21) . Adding considerations of time intervals (e.g.…”

mentioning

confidence: 99%

Identifying usual food choices at meals in overweight and obese study volunteers: implications for dietary advice

et al. 2018

Self Cite

View full text Add to dashboard Cite

Understanding food choices made for meals in overweight and obese individuals may aid strategies for weight loss tailored to their eating habits. However, limited studies have explored food choices at meal occasions. The aim of this study was to identify the usual food choices for meals of overweight and obese volunteers for a weight-loss trial. A cross-sectional analysis was performed using screening diet history data from a 12-month weight-loss trial (the HealthTrack study). A descriptive data mining tool, the Apriori algorithm of association rules, was applied to identify food choices at meal occasions using a nested hierarchical food group classification system. Overall, 432 breakfasts, 428 lunches, 432 dinners and 433 others (meals) were identified from the intake data (n 433 participants). A total of 142 items of closely related food clusters were identified at three food group levels. At the first sub-food group level, bread emerged as central to food combinations at lunch, but unprocessed meat appeared for this at dinner. The dinner meal was characterised by more varieties of vegetables and of foods in general. The definitions of food groups played a pivotal role in identifying food choice patterns at main meals. Given the large number of foods available, having an understanding of eating patterns in which key foods drive overall meal content can help translate and develop novel dietary strategies for weight loss at the individual level.

show abstract

“…The level of error associated with using food groups as opposed to using individual food items was determined. A 5-10% level of error was found when grouped foods were used (Burden et al, 2008) which was felt to be comparable to the error associated with faceto-face dietary assessments. It was then decided that all future analyses would be performed for grouped food data rather than individual food items.…”

Section: Introductionmentioning

confidence: 85%

Updating the DietAdvice website with new Australian food composition data

Probst

Jones

Lin

et al. 2009

Journal of Food Composition and Analysis

Self Cite

View full text Add to dashboard Cite

DietAdvice is an Australian self-administered dietary assessment website initially developed in [2003][2004][2005]. The website allows patients to enter their dietary information and dieticians to remotely access and interpret the data. DietAdvice is presently being updated with new Australian food composition data. This study aims to describe the update process for moving from 1995 to 2006 food composition data. The database for the website was developed using grouped food data from the NUTTAB 1995 database. All food groups were cross-matched with the food from the NUTTAB 2006 database using the food ID codes. Rules were applied to determine the suitability of the food for inclusion in the database. New, ungrouped foods were considered individually and added to existing groups or grouped together as new groups. Foods within each group were statistically weighted to determine the nutrient profile for each group. The NUTTAB 1995 data was used to develop 19, 103 and 422 first, second and third level groups, respectively. From the NUTTAB 2006 data, an additional 623 foods needed to be individually considered. The final database contained 23, 123 and 430 first, second and third level groups, respectively. Ensuring the most recent food composition data is incorporated into the database of the website will maximise the accuracy of the dietary advice provided by the dieticians.

show abstract

Identification of food groups for use in a self-administered, computer-assisted diet history interview for use in Australia

Cited by 9 publications

References 11 publications

Computerized dietary assessments compare well with interviewer administered diet histories for patients with type 2 diabetes mellitus in the primary healthcare setting

Computerized dietary assessments compare well with interviewer administered diet histories for patients with type 2 diabetes mellitus in the primary healthcare setting

Identifying usual food choices at meals in overweight and obese study volunteers: implications for dietary advice

Updating the DietAdvice website with new Australian food composition data

Contact Info

Product

Resources

About